OpenRaiser/Intern-Atlas huggingface dataset:
Exploring Wireless Sensor Network Technology In Sustainable Okra Garden: A Comparative Analysis Of Okra Grown In Different Fertilizer Treatments
Venue: arXiv cs.CY 2018
Year: 2018

The goal of this project was to explore commercial agricultural and irrigation sensor kits and to discern if the commercial wireless sensor network is viable tool for providing accurate real-time farm data at the nexus of food, energy and water. The smart garden consists of two different varieties of Abelmoschus esculentus planted in raised beds, each grown under two different fertilizer treatments. Soil watermark sensors were programmed to evaluate soil moisture and dictate irrigation events up to four times a day, while soil temperature and photosynthetic solar radiation sensors also recorded data every six hours. Solar panels harvested energy to power water pump and sensors. The objectives of the experiments were to evaluate and compare plant and soil parameters of the two okra varietes grown under two different fertilizer treatments. The plant parameters evaluated and compared were basal diameter, plant height, fruit production, and fruit size. Soil parameters measured were soil moisture, soil temperature, and soil nitrate concentration. The commercial sensors were evaluated on efficiency, accuracy, ease of use and overall practicality. Clemson spineless produced larger okra plants with the highest plant parameter values, followed by Emerald okra.
Joint Coarse-And-Fine Reasoning for Deep Optical Flow
Venue: arXiv cs.CV 2018
Year: 2018

We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets.
A Developer-Friendly Library for Smart Home IoT Privacy-Preserving Traffic Obfuscation
Venue: arXiv cs.CR 2018
Year: 2018

The number and variety of Internet-connected devices have grown enormously in the past few years, presenting new challenges to security and privacy. Research has shown that network adversaries can use traffic rate metadata from consumer IoT devices to infer sensitive user activities. Shaping traffic flows to fit distributions independent of user activities can protect privacy, but this approach has seen little adoption due to required developer effort and overhead bandwidth costs. Here, we present a Python library for IoT developers to easily integrate privacy-preserving traffic shaping into their products. The library replaces standard networking functions with versions that automatically obfuscate device traffic patterns through a combination of payload padding, fragmentation, and randomized cover traffic. Our library successfully preserves user privacy and requires approximately 4 KB/s overhead bandwidth for IoT devices with low send rates or high latency tolerances. This overhead is reasonable given normal Internet speeds in American homes and is an improvement on the bandwidth requirements of existing solutions.
On Distributed Storage Allocations of Large Files for Maximum Service Rate
Venue: arXiv cs.DC 2018
Year: 2018

Allocation of (redundant) file chunks throughout a distributed storage system affects important performance metrics such as the probability of file recovery, data download time, or the service rate of the system under a given data access model. This paper is concerned with the service rate under the assumption that the stored data is large and its download time is not negligible. We focus on quasi-uniform storage allocations and provide a service rate analysis for two common data access models. We find that the optimal allocation varies in accordance with different system parameters. This was not the case under the assumption that the download time does not scale with the size of data, where the minimal spreading allocation was previously found to be universally optimal.
Robust Counterfactual Inferences using Feature Learning and their Applications
Venue: arXiv cs.AI 2018
Year: 2018

In a wide variety of applications, including personalization, we want to measure the difference in outcome due to an intervention and thus have to deal with counterfactual inference. The feedback from a customer in any of these situations is only 'bandit feedback' - that is, a partial feedback based on whether we chose to intervene or not. Typically randomized experiments are carried out to understand whether an intervention is overall better than no intervention. Here we present a feature learning algorithm to learn from a randomized experiment where the intervention in consideration is most effective and where it is least effective rather than only focusing on the overall impact, thus adding a context to our learning mechanism and extract more information. From the randomized experiment, we learn the feature representations which divide the population into subpopulations where we observe statistically significant difference in average customer feedback between those who were subjected to the intervention and those who were not, with a level of significance l, where l is a configurable parameter in our model. We use this information to derive the value of the intervention in consideration for each instance in the population. With experiments, we show that using this additional learning, in future interventions, the context for each instance could be leveraged to decide whether to intervene or not.
Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models
Venue: arXiv cs.CV 2018
Year: 2018

Magnetic Resonance Spectroscopy (MRS) provides valuable information to help with the identification and understanding of brain tumors, yet MRS is not a widely available medical imaging modality. Aiming to counter this issue, this research draws on the advancements in machine learning techniques in other fields for the generation of artificial data. The generated methods were tested through the evaluation of their output against that of a real-world labelled MRS brain tumor data-set. Furthermore the resultant output from the generative techniques were each used to train separate traditional classifiers which were tested on a subset of the real MRS brain tumor dataset. The results suggest that there exist methods capable of producing accurate, ground truth based MRS voxels. These findings indicate that through generative techniques, large datasets can be made available for training deep, learning models for the use in brain tumor diagnosis.
Structured Interpretation of Temporal Relations
Venue: arXiv cs.CL 2018
Year: 2018

Temporal relations between events and time expressions in a document are often modeled in an unstructured manner where relations between individual pairs of time expressions and events are considered in isolation. This often results in inconsistent and incomplete annotation and computational modeling. We propose a novel annotation approach where events and time expressions in a document form a dependency tree in which each dependency relation corresponds to an instance of temporal anaphora where the antecedent is the parent and the anaphor is the child. We annotate a corpus of 235 documents using this approach in the two genres of news and narratives, with 48 documents doubly annotated. We report a stable and high inter-annotator agreement on the doubly annotated subset, validating our approach, and perform a quantitative comparison between the two genres of the entire corpus. We make this corpus publicly available.
Tomlinson-Harashima Precoding-Aided Multi-Antenna Non-Orthogonal Multiple Access
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, Tomlinson-Harashima Precoding (THP) is considered for multi-user multiple-input single-output (MU-MISO) non-orthogonal multiple access (NOMA) donwlink. Under the hierarchical structure in which multiple clusters each with two users are formed and served in the spatial domain and users in each cluster are served in the power domain, THP is applied to eliminate the inter-cluster interference (ICI) to the strong users and enlarge the dimension of the beam design space for mitigation of ICI to weak users as compared to conventional zero-forcing (ZF) inter-cluster beamforming. With the enlarged beam design space, two beam design algorithms for THP-aided MISO-NOMA are proposed. The first is a greedy sequential beam design with user scheduling, and the second is the joint beam redesign and power allocation. The two design problems lead to non-convex optimization problems. An efficient algorithm is proposed to solve the non-convex optimization problems based on successive convex approximation (SCA). Numerical results show that the proposed user scheduling and two beam design methods based on THP yield noticeable gain over existing methods.
Daily Stand-Up Meetings: Start Breaking the Rules
Venue: arXiv cs.SE 2018
Year: 2018

Members of high performing software teams collaborate, exchange information and coordinate their work on a frequent, regular basis. Most teams have the daily stand-up meeting as a central venue for these activities. Although this kind of meeting is one of the most popular agile practices, it has received little attention from researchers. We observed 102 daily stand-ups and interviewed 60 members of 15 teams in five countries. We found that the practice is usually challenging to conduct in a way that benefits the whole team. Many team members have a negative experience from conducting the meeting, which reduces job satisfaction, co-worker trust and well-being. However, the practice can be adjusted and improved to empower teams. In this article, we describe key factors that affect the meeting and propose four recommendations for improving the practice.
Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification
Venue: arXiv cs.CL 2018
Year: 2018

In this paper, we present Arap-Tweet, which is a large-scale and multi-dialectal corpus of Tweets from 11 regions and 16 countries in the Arab world representing the major Arabic dialectal varieties. To build this corpus, we collected data from Twitter and we provided a team of experienced annotators with annotation guidelines that they used to annotate the corpus for age categories, gender, and dialectal variety. During the data collection effort, we based our search on distinctive keywords that are specific to the different Arabic dialects and we also validated the location using Twitter API. In this paper, we report on the corpus data collection and annotation efforts. We also present some issues that we encountered during these phases. Then, we present the results of the evaluation performed to ensure the consistency of the annotation. The provided corpus will enrich the limited set of available language resources for Arabic and will be an invaluable enabler for developing author profiling tools and NLP tools for Arabic.
Optimal Precoder Designs for Sum-utility Maximization in SWIPT-enabled Multi-user MIMO Cognitive Radio Networks
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we propose a generalized framework that combines the cognitive radio (CR) techniques for spectrum sharing and the simultaneous wireless information and power transfer (SWIPT) for energy harvesting (EH) in the conventional multi-user MIMO (MuMIMO) channels, which leads to an MuMIMO-CR-SWIPT network. In this system, we have one secondary base-station (S-BS) that supports multiple secondary information decoding (S-ID) and secondary EH (S-EH) users simultaneously under the condition that interference power that affects the primary ID (P-ID) receivers should stay below a certain threshold. The goal of the paper is to develop a generalized precoder design that maximizes the sum-utility cost function under the transmit power constraint at the S-BS, and the EH constraint at each S-EH user, and the interference power constraint at each P-ID user. Therefore, the previous studies for the CR and SWIPT systems are casted as particular solutions of the proposed framework. The problem is inherently non-convex and even the weighted minimum mean squared error (WMMSE) transformation does not resolve the non-convexity of the original problem. To tackle the problem, we find a solution from the dual optimization via sub-gradient ellipsoid method based on the observation that the WMMSE transformation raises zero-duality gap between the primal and the dual problems. We also propose a simplified algorithm for the case of a single S-ID user, which is shown to achieve the global optimum. Finally, we demonstrate the optimality and efficiency of the proposed algorithms through numerical simulation results.
EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction
Venue: arXiv cs.CV 2018
Year: 2018

This paper details the sixth Emotion Recognition in the Wild (EmotiW) challenge. EmotiW 2018 is a grand challenge in the ACM International Conference on Multimodal Interaction 2018, Colorado, USA. The challenge aims at providing a common platform to researchers working in the affective computing community to benchmark their algorithms on `in the wild' data. This year EmotiW contains three sub-challenges: a) Audio-video based emotion recognition; b) Student engagement prediction; and c) Group-level emotion recognition. The databases, protocols and baselines are discussed in detail.
Light Ears: Information Leakage via Smart Lights
Venue: arXiv cs.CR 2018
Year: 2018

Modern Internet-enabled smart lights promise energy efficiency and many additional capabilities over traditional lamps. However, these connected lights create a new attack surface, which can be maliciously used to violate users' privacy and security. In this paper, we design and evaluate novel attacks that take advantage of light emitted by modern smart bulbs in order to infer users' private data and preferences. The first two attacks are designed to infer users' audio and video playback by a systematic observation and analysis of the multimedia-visualization functionality of smart light bulbs. The third attack utilizes the infrared capabilities of such smart light bulbs to create a covert-channel, which can be used as a gateway to exfiltrate user's private data out of their secured home or office network. A comprehensive evaluation of these attacks in various real-life settings confirms their feasibility and affirms the need for new privacy protection mechanisms.
High quality ultrasonic multi-line transmission through deep learning
Venue: arXiv cs.CV 2018
Year: 2018

Frame rate is a crucial consideration in cardiac ultrasound imaging and 3D sonography. Several methods have been proposed in the medical ultrasound literature aiming at accelerating the image acquisition. In this paper, we consider one such method called \textit{multi-line transmission} (MLT), in which several evenly separated focused beams are transmitted simultaneously. While MLT reduces the acquisition time, it comes at the expense of a heavy loss of contrast due to the interactions between the beams (cross-talk artifact). In this paper, we introduce a data-driven method to reduce the artifacts arising in MLT. To this end, we propose to train an end-to-end convolutional neural network consisting of correction layers followed by a constant apodization layer. The network is trained on pairs of raw data obtained through MLT and the corresponding \textit{single-line transmission} (SLT) data. Experimental evaluation demonstrates significant improvement both in the visual image quality and in objective measures such as contrast ratio and contrast-to-noise ratio, while preserving resolution unlike traditional apodization-based methods. We show that the proposed method is able to generalize well across different patients and anatomies on real and phantom data.
Peer to Peer Sharing of Distributed Energy Resources
Venue: arXiv cs.CE 2018
Year: 2018

As the penetration of distributed energy resources in the residential sector increases, the scope for sharing arrangements expands. We model a peer-to-peer rental market for rooftop solar and energy storage in the residential sector, with households seeking to minimize their electricity costs. For varying adoption levels, we characterize the market rental price, quantity, and participation rate. We find that up to 15% adoption, the peer-to-peer market generates a surplus comparable to that attainable though a centralized sharing model. The peer-to-peer market can incentivize an increase in total adoption in the long run. We find that direct subsidies would be a cheaper way to increase adoption if enabling the peer-to-peer market increases distribution grid costs by more than a few percent. This cost increase would be related to how locally the peer-to-peer market can match renters and owners. We compute metrics of this localness and find that the market clears quite locally for a wide range of adoption rates.
A Century Long Commitment to Assessing Artificial Intelligence and its Impact on Society
Venue: arXiv cs.AI 2018
Year: 2018

In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. The report, entitled "Artificial Intelligence and Life in 2030," examines eight domains of typical urban settings on which AI is likely to have impact over the coming years: transportation, home and service robots, healthcare, education, public safety and security, low-resource communities, employment and workplace, and entertainment. It aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI and its potential and to help guide decisions in industry and governments, as well as to inform research and development in the field. This article by the chair of the 2016 Study Panel and the inaugural chair of the AI100 Standing Committee describes the origins of this ambitious longitudinal study, discusses the framing of the inaugural report, and presents the report's main findings. It concludes with a brief description of the AI100 project's ongoing efforts and planned next steps.
LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations
Venue: arXiv cs.LG 2018
Year: 2018

Reinforcement learning approaches have long appealed to the data management community due to their ability to learn to control dynamic behavior from raw system performance. Recent successes in combining deep neural networks with reinforcement learning have sparked significant new interest in this domain. However, practical solutions remain elusive due to large training data requirements, algorithmic instability, and lack of standard tools. In this work, we introduce LIFT, an end-to-end software stack for applying deep reinforcement learning to data management tasks. While prior work has frequently explored applications in simulations, LIFT centers on utilizing human expertise to learn from demonstrations, thus lowering online training times. We further introduce TensorForce, a TensorFlow library for applied deep reinforcement learning exposing a unified declarative interface to common RL algorithms, thus providing a backend to LIFT. We demonstrate the utility of LIFT in two case studies in database compound indexing and resource management in stream processing. Results show LIFT controllers initialized from demonstrations can outperform human baselines and heuristics across latency metrics and space usage by up to 70%.
A Communication Protocol for Man-Machine Networks
Venue: arXiv cs.MA 2018
Year: 2018

One of the most challenging coordination problems in artificial intelligence is to achieve successful collaboration across large-scale heterogeneous systems that include Robots, Agents, and People (RAP). In the best case, these RAP systems are potentially capable of leveraging the strengths of the individual entities to achieve complex distributed tasks. However, without intelligent communication protocols, man-machine partnerships are likely to fail as the humans become overloaded with irrelevant information. This paper introduces a communication protocol for man machine systems and demonstrates that its message routing performance approaches the central optimized solution in a simulated smart environment scenario.
Linear complexity of generalized cyclotomic sequences of period $2p^{m}$
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we construct two generalized cyclotomic binary sequences of period $2p^{m}$ based on the generalized cyclotomy and compute their linear complexity, showing that they are of high linear complexity when $m\geq 2$.
The XDEM Multi-physics and Multi-scale Simulation Technology: Review on DEM-CFD Coupling, Methodology and Engineering Applications
Venue: arXiv cs.CE 2018
Year: 2018

The XDEM multi-physics and multi-scale simulation platform roots in the Ex- tended Discrete Element Method (XDEM) and is being developed at the In- stitute of Computational Engineering at the University of Luxembourg. The platform is an advanced multi- physics simulation technology that combines flexibility and versatility to establish the next generation of multi-physics and multi-scale simulation tools. For this purpose the simulation framework relies on coupling various predictive tools based on both an Eulerian and Lagrangian approach. Eulerian approaches represent the wide field of continuum models while the Lagrange approach is perfectly suited to characterise discrete phases. Thus, continuum models include classical simulation tools such as Computa- tional Fluid Dynamics (CFD) or Finite Element Analysis (FEA) while an ex- tended configuration of the classical Discrete Element Method (DEM) addresses the discrete e.g. particulate phase. Apart from predicting the trajectories of individual particles, XDEM extends the application to estimating the thermo- dynamic state of each particle by advanced and optimised algorithms. The thermodynamic state may include temperature and species distributions due to chemical reaction and external heat sources. Hence, coupling these extended features with either CFD or FEA opens up a wide range of applications as diverse as pharmaceutical industry e.g. drug production, agriculture food and processing industry, mining, construction and agricultural machinery, metals manufacturing, energy production and systems biology.